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                <h1 id="绘图"><a href="#绘图" class="headerlink" title="绘图"></a>绘图</h1><h2 id="绘图需要引入的包以及图片存储路径"><a href="#绘图需要引入的包以及图片存储路径" class="headerlink" title="绘图需要引入的包以及图片存储路径"></a>绘图需要引入的包以及图片存储路径</h2><p>import matplotlib.pyplot as plt<br>import matplotlib<br>from sklearn.metrics import *<br>import matplotlib.pyplot as plt<br>import numpy as np<br>exp_root = ‘/home/wyh/Python_workspace/DAN_EEGIMAGE_CODE/model_indices/‘</p>
<h2 id="横向对比图"><a href="#横向对比图" class="headerlink" title="横向对比图"></a>横向对比图</h2><pre><code>def Comparsion_regression():
    # 构建数据
    x_data = [&#39;愤怒&#39;, &#39;快乐&#39;, &#39;中立&#39;, &#39;悲伤&#39;, &#39;惊奇&#39;, &#39;厌恶&#39;, &#39;恐惧&#39;]
    y_data1 = [100.00, 92.31, 100.00, 83.33, 83.33, 50.00, 37.50]
    y_data2 = [100.00, 96.15, 100.00, 83.33, 91.67, 62.50, 37.50]
    bar_width = 0.4
    # Y轴数据使用range(len(x_data), 就是0、1、2...
    plt.barh(y=range(len(x_data)), width=y_data1, label=&#39;IMAGE&#39;, color=&#39;steelblue&#39;, alpha=0.8, height=bar_width)
    # Y轴数据使用np.arange(len(x_data))+bar_width,
    # 就是bar_width、1+bar_width、2+bar_width...这样就和第一个柱状图并列了
    plt.barh(y=np.arange(len(x_data)) + bar_width, width=y_data2, label=&#39;EEG-LIKE&#39;, color=&#39;indianred&#39;, alpha=0.8,
             height=bar_width)
    # 在柱状图上显示具体数值, ha参数控制水平对齐方式, va控制垂直对齐方式
    for y, x in enumerate(y_data1):
        plt.text(x - 6, y - bar_width / 2, &#39;%s&#39; % (x), ha=&#39;center&#39;, va=&#39;bottom&#39;)
    for y, x in enumerate(y_data2):
        plt.text(x - 6, y + bar_width / 2, &#39;%s&#39; % (x), ha=&#39;center&#39;, va=&#39;bottom&#39;)
    # 为Y轴设置刻度值
    plt.yticks(np.arange(len(x_data)) + bar_width / 2, x_data)
    # 设置标题
    plt.title(&quot;Comparison graph of accuracy before and after regression&quot;)
    # 为两条坐标轴设置名称
    plt.xlabel(&quot;Accuracy(%)&quot;)
    plt.ylabel(&quot;Emotional categories&quot;)

    plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.2)
    # 显示图例
    plt.legend(loc=&#39;upper right&#39;)
    plt.savefig(&quot;/home/wyh/Python_workspace/DAN_EEGIMAGE_CODE/model_indices/DualChart.jpg&quot;)
    plt.show()
</code></pre>
<p><img src="/images/DualChart.jpg" alt="横向对比图"></p>
<h2 id="混淆矩阵"><a href="#混淆矩阵" class="headerlink" title="混淆矩阵"></a>混淆矩阵</h2><pre><code>def plot_confusion_matrix(y_true, y_pred, labels):  # 计算混淆矩阵，以评估分类的准确性。
    cmap = plt.cm.binary
    cm = confusion_matrix(y_true, y_pred)
    tick_marks = np.array(range(len(labels))) + 0.5
    np.set_printoptions(precision=2)
    cm_normalized = cm.astype(&#39;float&#39;) / cm.sum(axis=1)[:, np.newaxis]
    plt.figure(figsize=(20, 18), dpi=120)
    ind_array = np.arange(len(labels))
    x, y = np.meshgrid(ind_array, ind_array)
    intFlag = 0
    for x_val, y_val in zip(x.flatten(), y.flatten()):
        #
        if (intFlag):
            c = cm[y_val][x_val]
            plt.text(x_val, y_val, &quot;%d&quot; % (c,), color=&#39;black&#39;, fontsize=20, va=&#39;center&#39;, ha=&#39;center&#39;)

        else:
            c = cm_normalized[y_val][x_val]

            if (c &gt; 0.80):

                plt.text(x_val, y_val, &quot;%0.2f&quot; % (c * 100,), color=&#39;white&#39;, fontsize=20, va=&#39;center&#39;, ha=&#39;center&#39;)
            else:
                plt.text(x_val, y_val, &quot;%0.2f&quot; % (c * 100,), color=&#39;black&#39;, fontsize=20, va=&#39;center&#39;, ha=&#39;center&#39;)
    if (intFlag):
        plt.imshow(cm, interpolation=&#39;nearest&#39;, cmap=cmap)
    else:
        plt.imshow(cm_normalized, interpolation=&#39;nearest&#39;, cmap=cmap)
    plt.gca().set_xticks(tick_marks, minor=True)
    plt.gca().set_yticks(tick_marks, minor=True)
    plt.gca().xaxis.set_ticks_position(&#39;none&#39;)
    plt.gca().yaxis.set_ticks_position(&#39;none&#39;)
    plt.grid(True, which=&#39;minor&#39;, linestyle=&#39;-&#39;)
    plt.gcf().subplots_adjust(bottom=0.15)
    plt.title(&#39;&#39;)
    plt.colorbar()
    xlocations = np.array(range(len(labels)))
    plt.xticks(xlocations, labels, rotation=90, fontsize=20, fontproperties=zhfont1)
    plt.yticks(xlocations, labels, fontsize=20, fontproperties=zhfont1)
    plt.ylabel(&#39;真实类别&#39;, fontsize=20, fontproperties=zhfont1)
    plt.xlabel(&#39;预测类别&#39;, fontsize=20, fontproperties=zhfont1)
    plt.savefig(exp_root + &#39;knn_confusion_matrix12.jpg&#39;, dpi=600)
    plt.show()
</code></pre>
<p><img src="/images/knn_confusion_matrix12.jpg" alt="混淆矩阵图"></p>
<h2 id="t-sne"><a href="#t-sne" class="headerlink" title="t-sne"></a>t-sne</h2><pre><code>def plot_embedding(data, label):
    fig = plt.figure(figsize=(5, 5))
    type1_x = []
    type1_y = []
    type2_x = []
    type2_y = []
    type3_x = []
    type3_y = []
    type4_x = []
    type4_y = []
    type5_x = []
    type5_y = []
    type6_x = []
    type6_y = []
    type7_x = []
    type7_y = []
    type8_x = []
    type8_y = []
    for i in range(data.shape[0]):
        if label[i] == 0:
            type1_x.append(data[i][0])
            type1_y.append(data[i][1])
        if label[i] == 1:
            type2_x.append(data[i][0])
            type2_y.append(data[i][1])
        if label[i] == 2:
            type3_x.append(data[i][0])
            type3_y.append(data[i][1])
        if label[i] == 3:
            type4_x.append(data[i][0])
            type4_y.append(data[i][1])
        if label[i] == 4:
            type5_x.append(data[i][0])
            type5_y.append(data[i][1])
        if label[i] == 5:
            type6_x.append(data[i][0])
            type6_y.append(data[i][1])
        if label[i] == 6:
            type7_x.append(data[i][0])
            type7_y.append(data[i][1])
        if label[i] == 7:
            type8_x.append(data[i][0])
            type8_y.append(data[i][1])
    color = plt.cm.Set3(0)
    color = np.array(color).reshape(1, 4)
    color1 = plt.cm.Set3(1)
    color1 = np.array(color1).reshape(1, 4)
    color2 = plt.cm.Set3(2)
    color2 = np.array(color2).reshape(1, 4)
    color3 = plt.cm.Set3(3)
    color3 = np.array(color3).reshape(1, 4)
    type1 = plt.scatter(type1_x, type1_y, s=10, c=&#39;r&#39;)
    type2 = plt.scatter(type2_x, type2_y, s=10, c=&#39;g&#39;)
    type3 = plt.scatter(type3_x, type3_y, s=10, c=&#39;b&#39;)
    type4 = plt.scatter(type4_x, type4_y, s=10, c=&#39;k&#39;)
    type5 = plt.scatter(type5_x, type5_y, s=10, c=&#39;c&#39;)
    type6 = plt.scatter(type6_x, type6_y, s=10, c=&#39;m&#39;)
    type7 = plt.scatter(type7_x, type7_y, s=10, c=&#39;y&#39;)
    type8 = plt.scatter(type8_x, type8_y, s=10, c=color)
    plt.legend((type1, type2, type3, type4, type5, type6, type7, type8),
               (&#39;愤怒&#39;, &#39;厌恶&#39;, &#39;恐惧&#39;, &#39;快乐&#39;, &#39;中立&#39;, &#39;悲伤&#39;, &#39;惊奇&#39;), prop=zhfont1)
    plt.xticks()
    plt.yticks()
    return fig
def visual_features(x_train_img, x_test_img, y_train, y_test):
    from sklearn.manifold import TSNE
    # 可视化训练/测试数据模式
    X_train = x_train_img
    Y_train = y_train
    tsne = TSNE(n_components=2, init=&#39;pca&#39;, random_state=0)  # 使用TSNE对特征降到二维
    reduce_dim_X = tsne.fit_transform(X_train)  # 降维后的数据
    # 画图
    # plt.subplot(1, 2, 1)
    fig = plot_embedding(reduce_dim_X, Y_train)
    # 图例过大，保存figure时无法保存完全，故对此参数进行调整
    fig.subplots_adjust(right=0.7)
    plt.title(&#39;图像视觉特征可视化&#39;, fontproperties=zhfont1)
    plt.savefig(&quot;/home/wyh/Python_workspace/DAN_EEGIMAGE_CODE/model_indices/t-sne_train_image.jpg&quot;)
    plt.show()
    X_test = x_test_img
    Y_test = y_test
    tsne = TSNE(n_components=2, init=&#39;pca&#39;, random_state=0)  # 使用TSNE对特征降到二维
    reduce_dim_X = tsne.fit_transform(X_test)  # 降维后的数据
    # 画图
    # plt.subplot(1, 2, 2)
    fig = plot_embedding(reduce_dim_X, Y_test)
    # 图例过大，保存figure时无法保存完全，故对此参数进行调整
    fig.subplots_adjust(right=0.7)
    plt.title(&#39;虚拟脑电情感特征可视化&#39;, fontproperties=zhfont1)
    # plt.show()
    plt.savefig(&quot;/home/wyh/Python_workspace/DAN_EEGIMAGE_CODE/model_indices/t-sne_train_EEG-LIKE.jpg&quot;)
    plt.show()
</code></pre>
<p><img src="/images/t-sne_train_image.jpg" alt="t-sne图1"><br><img src="/images/t-sne_train_EEG-LIKE.jpg" alt="t-sne图2"></p>
<h2 id="纵向对比图"><a href="#纵向对比图" class="headerlink" title="纵向对比图"></a>纵向对比图</h2><pre><code>def comparison_bar(IMAGE_score, Regression_score, Fusion_score, EEG_score):
    X = [&#39;图像&#39;, &#39;回归&#39;, &#39;融合&#39;, &#39;脑电&#39;]
    Y = [0.839080459770114, 0.873563218390804, 0.885057471264367, 0.977011494252873]

    plt.xticks(fontproperties=zhfont1)
    plt.bar(X, Y, 0.5, color=[&#39;green&#39;, &#39;blue&#39;, &#39;yellow&#39;, &#39;red&#39;])
    plt.xlabel(&quot;方法&quot;, fontproperties=zhfont1)
    plt.ylabel(&quot;准确率&quot;, fontproperties=zhfont1)
    plt.title(&quot;方法对比图&quot;, fontproperties=zhfont1)
    for a, b in zip(X, Y):
        plt.text(a, b, &#39;%.4f&#39; % b, ha=&#39;center&#39;, va=&#39;bottom&#39;, fontsize=11)
    # plt.show()
    plt.savefig(&quot;/home/wyh/Python_workspace/DAN_EEGIMAGE_CODE/model_indices/BarChart.jpg&quot;)
    plt.show()
</code></pre>
<p><img src="/images/BarChart.jpg" alt="纵向对比图"></p>
<h2 id="折线图"><a href="#折线图" class="headerlink" title="折线图"></a>折线图</h2><pre><code>def EEG_like(x_test_eeg, y_pred_test):
    x_test_eeg_average = x_test_eeg.mean(0)
    x_test_like_average = y_pred_test.mean(0)
    x1 = range(0, 128)
    x2 = range(0, 128)
    y1 = x_test_eeg_average
    y2 = x_test_like_average
    plt.subplot(2, 1, 1)
    plt.plot(x1, y1, &#39;.-&#39;)
    plt.title(&#39;脑电情感特征和虚拟脑电情感特征&#39;, fontproperties=zhfont1)
    plt.ylabel(&#39;脑电情感特征&#39;, fontproperties=zhfont1)
    # plt.xlabel(&#39;Time series&#39;,fontproperties=zhfont1)
    plt.subplot(2, 1, 2)
    plt.plot(x2, y2, &#39;.-&#39;)
    plt.xlabel(&#39;模拟时间序列&#39;, fontproperties=zhfont1)
    plt.ylabel(&#39;虚拟脑电情感特征&#39;, fontproperties=zhfont1)
    # plt.show()
    plt.savefig(&quot;/home/wyh/Python_workspace/DAN_EEGIMAGE_CODE/model_indices/EEG-LIKE.jpg&quot;)
    plt.show()
</code></pre>
<p><img src="/images/EEG-LIKE.jpg" alt="折线图"></p>
<h2 id="accuracy-loss图"><a href="#accuracy-loss图" class="headerlink" title="accuracy_loss图"></a>accuracy_loss图</h2><pre><code>x1 = range(0, iteration//log_interval)
x2 = range(0, iteration//log_interval)
y1 = Accuracy_list
y2 = Loss_list
plt.subplot(2, 1, 1)
plt.plot(x1, y1, &#39;o-&#39;)
plt.title(&#39;Train accuracy vs. epochs&#39;)
plt.ylabel(&#39;Train accuracy&#39;)
plt.subplot(2, 1, 2)
plt.plot(x2, y2, &#39;.-&#39;)
plt.xlabel(&#39;Train loss vs. epochs&#39;)
plt.ylabel(&#39;Train loss&#39;)
plt.savefig(&quot;/home/wyh/Python_workspace/DAN_EEGIMAGE_CODE/model_indices/accuracy_loss.jpg&quot;)
plt.show()
</code></pre>
<p><img src="/images/accuracy_loss.jpg" alt="accuracy_loss图"></p>

                
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$(function () {
    var searchFunc = function (path, search_id, content_id) {
        'use strict';
        $.ajax({
            url: path,
            dataType: "xml",
            success: function (xmlResponse) {
                // get the contents from search data
                var datas = $("entry", xmlResponse).map(function () {
                    return {
                        title: $("title", this).text(),
                        content: $("content", this).text(),
                        url: $("url", this).text()
                    };
                }).get();
                var $input = document.getElementById(search_id);
                var $resultContent = document.getElementById(content_id);
                $input.addEventListener('input', function () {
                    var str = '<ul class=\"search-result-list\">';
                    var keywords = this.value.trim().toLowerCase().split(/[\s\-]+/);
                    $resultContent.innerHTML = "";
                    if (this.value.trim().length <= 0) {
                        return;
                    }
                    // perform local searching
                    datas.forEach(function (data) {
                        var isMatch = true;
                        var data_title = data.title.trim().toLowerCase();
                        var data_content = data.content.trim().replace(/<[^>]+>/g, "").toLowerCase();
                        var data_url = data.url;
                        data_url = data_url.indexOf('/') === 0 ? data.url : '/' + data_url;
                        var index_title = -1;
                        var index_content = -1;
                        var first_occur = -1;
                        // only match artiles with not empty titles and contents
                        if (data_title !== '' && data_content !== '') {
                            keywords.forEach(function (keyword, i) {
                                index_title = data_title.indexOf(keyword);
                                index_content = data_content.indexOf(keyword);
                                if (index_title < 0 && index_content < 0) {
                                    isMatch = false;
                                } else {
                                    if (index_content < 0) {
                                        index_content = 0;
                                    }
                                    if (i === 0) {
                                        first_occur = index_content;
                                    }
                                }
                            });
                        }
                        // show search results
                        if (isMatch) {
                            str += "<li><a href='" + data_url + "' class='search-result-title'>" + data_title + "</a>";
                            var content = data.content.trim().replace(/<[^>]+>/g, "");
                            if (first_occur >= 0) {
                                // cut out 100 characters
                                var start = first_occur - 20;
                                var end = first_occur + 80;
                                if (start < 0) {
                                    start = 0;
                                }
                                if (start === 0) {
                                    end = 100;
                                }
                                if (end > content.length) {
                                    end = content.length;
                                }
                                var match_content = content.substr(start, end);
                                // highlight all keywords
                                keywords.forEach(function (keyword) {
                                    var regS = new RegExp(keyword, "gi");
                                    match_content = match_content.replace(regS, "<em class=\"search-keyword\">" + keyword + "</em>");
                                });

                                str += "<p class=\"search-result\">" + match_content + "...</p>"
                            }
                            str += "</li>";
                        }
                    });
                    str += "</ul>";
                    $resultContent.innerHTML = str;
                });
            }
        });
    };

    searchFunc('/search.xml', 'searchInput', 'searchResult');
});
</script>

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